X-Git-Url: http://review.tizen.org/git/?a=blobdiff_plain;f=modules%2Fdnn%2Fsrc%2Flayers%2Fmax_unpooling_layer.cpp;h=a44d25ce899c5d96ce0b450036ae8b1c1e70977c;hb=613c12e59015f4bd7909916ceee195edd7ef88d0;hp=daac57854c6c125d49fa9bc33cfb47dc9f16e7cc;hpb=c231472ad6672082f11e7447cf52631e19d7d7dc;p=platform%2Fupstream%2Fopencv.git

diff --git a/modules/dnn/src/layers/max_unpooling_layer.cpp b/modules/dnn/src/layers/max_unpooling_layer.cpp
index daac578..a44d25c 100644
--- a/modules/dnn/src/layers/max_unpooling_layer.cpp
+++ b/modules/dnn/src/layers/max_unpooling_layer.cpp
@@ -11,17 +11,21 @@ Implementation of Batch Normalization layer.
 
 #include "../precomp.hpp"
 #include "layers_common.hpp"
-#include "op_halide.hpp"
+#include "../op_cuda.hpp"
+#include "../op_halide.hpp"
 #include <opencv2/dnn/shape_utils.hpp>
 
-#include <iostream>
+#ifdef HAVE_CUDA
+#include "../cuda4dnn/primitives/max_unpooling.hpp"
+using namespace cv::dnn::cuda4dnn;
+#endif
 
 namespace cv
 {
 namespace dnn
 {
 
-class MaxUnpoolLayerImpl : public MaxUnpoolLayer
+class MaxUnpoolLayerImpl CV_FINAL : public MaxUnpoolLayer
 {
 public:
     MaxUnpoolLayerImpl(const LayerParams& params)
@@ -32,24 +36,30 @@ public:
         poolStride = Size(params.get<int>("pool_stride_w"), params.get<int>("pool_stride_h"));
     }
 
-    virtual bool supportBackend(int backendId)
+    virtual bool supportBackend(int backendId) CV_OVERRIDE
     {
-        return backendId == DNN_BACKEND_DEFAULT ||
-               backendId == DNN_BACKEND_HALIDE && haveHalide() &&
-               !poolPad.width && !poolPad.height;
+        return backendId == DNN_BACKEND_OPENCV ||
+               backendId == DNN_BACKEND_CUDA ||
+               (backendId == DNN_BACKEND_HALIDE && haveHalide() && !poolPad.width && !poolPad.height);
     }
 
     bool getMemoryShapes(const std::vector<MatShape> &inputs,
                          const int requiredOutputs,
                          std::vector<MatShape> &outputs,
-                         std::vector<MatShape> &internals) const
+                         std::vector<MatShape> &internals) const CV_OVERRIDE
     {
-        CV_Assert(inputs.size() == 2);
+        CV_Assert(inputs.size() == 2 || inputs.size() == 3);
         CV_Assert(total(inputs[0]) == total(inputs[1]));
 
-        MatShape outShape = inputs[0];
-        outShape[2] = (outShape[2] - 1) * poolStride.height + poolKernel.height - 2 * poolPad.height;
-        outShape[3] = (outShape[3] - 1) * poolStride.width + poolKernel.width - 2 * poolPad.width;
+        MatShape outShape;
+        if (inputs.size() == 2)
+        {
+            outShape = inputs[0];
+            outShape[2] = (outShape[2] - 1) * poolStride.height + poolKernel.height - 2 * poolPad.height;
+            outShape[3] = (outShape[3] - 1) * poolStride.width + poolKernel.width - 2 * poolPad.width;
+        }
+        else
+            outShape = inputs[2];
 
         outputs.clear();
         outputs.push_back(outShape);
@@ -57,22 +67,24 @@ public:
         return false;
     }
 
-    void forward(InputArrayOfArrays inputs_arr, OutputArrayOfArrays outputs_arr, OutputArrayOfArrays internals_arr)
+    void forward(InputArrayOfArrays inputs_arr, OutputArrayOfArrays outputs_arr, OutputArrayOfArrays internals_arr) CV_OVERRIDE
     {
         CV_TRACE_FUNCTION();
         CV_TRACE_ARG_VALUE(name, "name", name.c_str());
 
-        Layer::forward_fallback(inputs_arr, outputs_arr, internals_arr);
-    }
+        if (inputs_arr.depth() == CV_16S)
+        {
+            forward_fallback(inputs_arr, outputs_arr, internals_arr);
+            return;
+        }
 
-    void forward(std::vector<Mat*> &inputs, std::vector<Mat> &outputs, std::vector<Mat> &internals)
-    {
-        CV_TRACE_FUNCTION();
-        CV_TRACE_ARG_VALUE(name, "name", name.c_str());
+        std::vector<Mat> inputs, outputs;
+        inputs_arr.getMatVector(inputs);
+        outputs_arr.getMatVector(outputs);
 
-        CV_Assert(inputs.size() == 2);
-        Mat& input = *inputs[0];
-        Mat& indices = *inputs[1];
+        CV_Assert(inputs.size() == 2 || inputs.size() == 3);
+        Mat& input = inputs[0];
+        Mat& indices = inputs[1];
 
         CV_Assert(input.total() == indices.total());
         CV_Assert(input.size[0] == 1);
@@ -103,6 +115,7 @@ public:
                             << "i_c=" << i_c << std::endl
                             << "i_wh=" << i_wh << std::endl
                             << "index=" << index << std::endl
+                            << "maxval=" << inptr[i_wh] << std::endl
                             << "outPlaneTotal=" << outPlaneTotal << std::endl
                             << "input.size=" << input.size << std::endl
                             << "indices.size=" << indices.size << std::endl
@@ -116,7 +129,36 @@ public:
         }
     }
 
-    virtual Ptr<BackendNode> initHalide(const std::vector<Ptr<BackendWrapper> > &input)
+#ifdef HAVE_CUDA
+    Ptr<BackendNode> initCUDA(
+        void *context_,
+        const std::vector<Ptr<BackendWrapper>>& inputs,
+        const std::vector<Ptr<BackendWrapper>>& outputs
+    ) override
+    {
+        auto context = reinterpret_cast<csl::CSLContext*>(context_);
+
+        cuda4dnn::MaxUnpoolingConfiguration config;
+        auto& window_size = config.window_size;
+        window_size.resize(2);
+        window_size[0] = poolKernel.height;
+        window_size[1] = poolKernel.width;
+
+        auto& strides = config.strides;
+        strides.resize(2);
+        strides[0] = poolStride.height;
+        strides[1] = poolStride.width;
+
+        auto& pads_begin = config.pads_begin;
+        pads_begin.resize(2);
+        pads_begin[0] = poolPad.height;
+        pads_begin[1] = poolPad.width;
+
+        return make_cuda_node<cuda4dnn::MaxUnpoolingOp>(preferableTarget, std::move(context->stream), config);
+    }
+#endif
+
+    virtual Ptr<BackendNode> initHalide(const std::vector<Ptr<BackendWrapper> > &input) CV_OVERRIDE
     {
 #ifdef HAVE_HALIDE
         // Meaningless operation if false because if kernel > stride